We evaluate three different approaches for computing optical flow,
namely, PWC-Net [1]}, RAFT [2]} and ARFlow [3]};
the first two are supervised, while the latter is self-supervised.
We extract the optical flow at the original resolution of the image pairs,
with the frame gaps \(n \in \lbrace -2, -1, 1, 2\rbrace \)  for all datasets,
except for FBMS59, where we use \(n \in \lbrace -6, -3, 3, 6\rbrace \)  to compensate for small motion.
To generate inputs to the network for training, the flows are resized to \(128 \times 224\)  (and scaled accordingly),
converted to 3-channel images with the standard visualization used for optical flow,
and normalized to \([-1,1]\) .
